Burnout symptoms detection and prediction

ABSTRACT

A method for detecting and presenting burnout symptoms associated with users is provided. The method may include receiving a plurality of predictor rules for monitoring and detecting a plurality of burnout indicators. The method may further include monitoring and receiving the plurality of burnout indicators. Additionally, the method may include storing the monitored and received plurality of burnout indicators. The method may also include detecting a plurality of burnout events associated with the stored monitored and received plurality of burnout indicators. The method may further include determining the plurality of burnout symptoms based on the detected plurality of burnout events. The method may also include determining at least one aggregate score based on the determined plurality of burnout symptoms. The method may further include presenting at least one alert and at least one recommendation based on the determined plurality of burnout symptoms and the determined at least one aggregate score.

BACKGROUND

The present invention relates generally to the field of computing, andmore specifically, to data collection and analysis.

Generally, burnout is a state of emotional, mental, and physicalexhaustion caused by excessive and prolonged stress that may occur toindividuals when feeling overwhelmed and unable to meet constantdemands. Typically, causes of burnout may be based on work-relatedstress and activity, individual lifestyle, and personality traits.Specifically, factors that contribute to burnout may include overlydemanding job expectations, overwhelming responsibilities, lack ofsleep, lack of a social life, and pessimism. Furthermore, the factorsthat contribute to burnout may lead to burnout symptoms such as fatigue,head and muscle aches, emotional detachment, withdrawal, and changes ineating and sleeping patterns. Typical burnout detection techniques focuson the physical conditions associated with burnouts, and may includemeasuring changes in heartbeat and physical fatigue.

SUMMARY

A method for detecting and presenting a plurality of burnout symptomsassociated with at least one user is provided. The method may includereceiving a plurality of predictor rules for monitoring and detecting aplurality of burnout indicators. The method may further includemonitoring and receiving the plurality of burnout indicators.Additionally, the method may include storing the monitored and receivedplurality of burnout indicators. The method may also include detecting aplurality of burnout events associated with the stored monitored andreceived plurality of burnout indicators. The method may further includedetermining the plurality of burnout symptoms based on the detectedplurality of burnout events. The method may also include determining atleast one aggregate score based on the determined plurality of burnoutsymptoms. The method may further include presenting at least one alertand at least one recommendation based on the determined plurality ofburnout symptoms and the determined at least one aggregate score.

A computer system for detecting and presenting a plurality of burnoutsymptoms associated with at least one user is provided. The computersystem may include one or more processors, one or more computer-readablememories, one or more computer-readable tangible storage devices, andprogram instructions stored on at least one of the one or more storagedevices for execution by at least one of the one or more processors viaat least one of the one or more memories, whereby the computer system iscapable of performing a method. The method may include receiving aplurality of predictor rules for monitoring and detecting a plurality ofburnout indicators. The method may further include monitoring andreceiving the plurality of burnout indicators. Additionally, the methodmay include storing the monitored and received plurality of burnoutindicators. The method may also include detecting a plurality of burnoutevents associated with the stored monitored and received plurality ofburnout indicators. The method may further include determining theplurality of burnout symptoms based on the detected plurality of burnoutevents. The method may also include determining at least one aggregatescore based on the determined plurality of burnout symptoms. The methodmay further include presenting at least one alert and at least onerecommendation based on the determined plurality of burnout symptoms andthe determined at least one aggregate score.

A computer program product for detecting and presenting a plurality ofburnout symptoms associated with at least one user is provided. Thecomputer program product may include one or more computer-readablestorage devices and program instructions stored on at least one of theone or more tangible storage devices, the program instructionsexecutable by a processor. The computer program product may includeprogram instructions to receive a plurality of predictor rules formonitoring and detecting a plurality of burnout indicators. The computerprogram product may further include program instructions to monitor andreceive the plurality of burnout indicators. Additionally, the computerprogram product may also include program instructions to store themonitored and received plurality of burnout indicators. The computerprogram product may further include program instructions to detect aplurality of burnout events associated with the stored monitored andreceived plurality of burnout indicators. The computer program productmay also include program instructions to determine the plurality ofburnout symptoms based on the detected plurality of burnout events. Thecomputer program product may further include program instructions todetermine at least one aggregate score based on the determined pluralityof burnout symptoms. The computer program product may also includeprogram instructions to present at least one alert and at least onerecommendation based on the determined plurality of burnout symptoms andthe determined at least one aggregate score.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

These and other objects, features and advantages of the presentinvention will become apparent from the following detailed descriptionof illustrative embodiments thereof, which is to be read in connectionwith the accompanying drawings. The various features of the drawings arenot to scale as the illustrations are for clarity in facilitating oneskilled in the art in understanding the invention in conjunction withthe detailed description. In the drawings:

FIG. 1 illustrates a networked computer environment according to oneembodiment;

FIG. 2 is a block diagram illustrating the system architecture ofburnout detection and prediction program according to one embodiment;

FIG. 3 is an operational flowchart illustrating the steps carried out bya program for detecting and presenting burnout symptoms according to oneembodiment;

FIG. 4 is a block diagram of the system architecture of a program fordetecting and presenting burnout symptoms according to one embodiment;

FIG. 5 is a block diagram of an illustrative cloud computing environmentincluding the computer system depicted in FIG. 1, in accordance with anembodiment of the present disclosure; and

FIG. 6 is a block diagram of functional layers of the illustrative cloudcomputing environment of FIG. 5, in accordance with an embodiment of thepresent disclosure.

DETAILED DESCRIPTION

Detailed embodiments of the claimed structures and methods are disclosedherein; however, it can be understood that the disclosed embodiments aremerely illustrative of the claimed structures and methods that may beembodied in various forms. This invention may, however, be embodied inmany different forms and should not be construed as limited to theexemplary embodiments set forth herein. In the description, details ofwell-known features and techniques may be omitted to avoid unnecessarilyobscuring the presented embodiments.

Embodiments of the present invention relate generally to the field ofcomputing, and more particularly, to data collection and analysis. Thefollowing described exemplary embodiments provide a system, method andprogram product for detecting burnout symptoms. Therefore, the presentembodiment has the capacity to improve the technical field associatedwith burnout symptoms detection by monitoring and receiving sets ofbehavior indicators. Specifically, the present embodiment may usedetectors to monitor and receive behavior indicators, and may usepredictors to detect events associated with the behavior indicators todetermine whether burnout symptoms are present.

As previously described with respect to burnout symptoms, burnouts maybe based on work-related stress and activity, lifestyle, and personalitytraits. Furthermore, the factors that contribute to burnouts may lead toburnout symptoms such as fatigue, head and muscle aches, emotionaldetachment, withdrawal, and changes in eating and sleeping patterns.However, as previously described, current burnout detection techniquesare typically limited to the physical conditions associated withburnouts and may be obtrusive, such as measuring changes in heartbeatand physical fatigue. Therefore, the current burnout detectiontechniques may be limited in detecting burnouts, and predicting whenburnouts occur, based on the limited information derived from measuringphysical conditions as opposed to detecting and measuring additionalinformation such as lifestyle and personality traits. As such, it may beadvantageous, among other things, to provide a system, method andprogram product for detecting burnout symptoms by monitoring userpsychological, sociological and working environment conditions.Specifically, the present embodiment may use detectors to detect andmeasure working activities, social activities, and personality traitsassociated with users, and may use predictors to measure changes in thedetected and measured working activities, social activities, andpersonality traits, to determine whether burnout symptoms are present.

According to at least one implementation of the present embodiment,predictor rules for monitoring and receiving burnout indicators may bereceived. Then, burnout indicators may be monitored and received. Next,the monitored and received burnout indictors may be stored. Then,burnout events associated with the stored monitored and received burnoutindicators may be detected based on the received predictor rules.Thereafter, based on the detected burnout events, burnout symptoms maybe determined. Next, aggregate scores based on the determined burnoutsymptoms may be determined. Then, alerts may be presented based on thedetermined burnout symptoms and the aggregate scores.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The following described exemplary embodiments provide a system, methodand program product for determining and providing aggregate functionsassociated with database tables.

According to at least one implementation, predictor rules for monitoringand receiving burnout indicators may be received. Then, burnoutindicators may be monitored and received. Next, the monitored andreceived burnout indictors may be stored. Then, burnout eventsassociated with the stored monitored and received burnout indicators maybe detected based on the received predictor rules. Thereafter, based onthe detected burnout events, burnout symptoms may be determined. Next,aggregate scores based on the determined burnout symptoms may bedetermined. Then, alerts may be presented based on the determinedburnout symptoms and the aggregate scores.

Referring now to FIG. 1, an exemplary networked computer environment 100in accordance with one embodiment is depicted. The networked computerenvironment 100 may include a computer 102 with a processor 104 and adata storage device 106 that is enabled to run a burnout detection andprediction program 108A and a software program 114. The software program114 may be an application program such as an internet browser and anemail program. The burnout detection and prediction program 108A maycommunicate with the software program 114. The networked computerenvironment 100 may also include a server 112 that is enabled to run aburnout detection and prediction program 108B and a communicationnetwork 110. The networked computer environment 100 may include aplurality of computers 102 and servers 112, only one of which is shownfor illustrative brevity.

According to at least one implementation, the present embodiment mayalso include a database 116, which may be running on server 112. Thecommunication network 110 may include various types of communicationnetworks, such as a wide area network (WAN), local area network (LAN), atelecommunication network, a wireless network, a public switched networkand/or a satellite network. It may be appreciated that FIG. 1 providesonly an illustration of one implementation and does not imply anylimitations with regard to the environments in which differentembodiments may be implemented. Many modifications to the depictedenvironments may be made based on design and implementationrequirements.

The client computer 102 may communicate with server computer 112 via thecommunications network 110. The communications network 110 may includeconnections, such as wire, wireless communication links, or fiber opticcables. As will be discussed with reference to FIG. 4, server computer112 may include internal components 800 a and external components 900 a,respectively, and client computer 102 may include internal components800 b and external components 900 b, respectively. Server computer 112may also operate in a cloud computing service model, such as Software asa Service (SaaS), Platform as a Service (PaaS), or Infrastructure as aService (IaaS). Server 112 may also be located in a cloud computingdeployment model, such as a private cloud, community cloud, publiccloud, or hybrid cloud. Client computer 102 may be, for example, amobile device, a telephone, a personal digital assistant, a netbook, alaptop computer, a tablet computer, a desktop computer, or any type ofcomputing device capable of running a program and accessing a network.According to various implementations of the present embodiment, theburnout detection and prediction program 108A, 108B may interact with adatabase 116 that may be embedded in various storage devices, such as,but not limited to a mobile device 102, a networked server 112, or acloud storage service.

According to the present embodiment, a program, such as a burnoutdetection and prediction program 108A and 108B, may run on the clientcomputer 102 or on the server computer 112 via a communications network110. The burnout detection and prediction program 108A, 108B may detectburnout symptoms. Specifically, a user using a computer, such ascomputer 102, may run a burnout detection and prediction program 108A,108B that interacts with a software program 114, such as an emailprogram, to monitor and receive burnout indicators associated withusers, detect changes to the burnout indicators to determine whetherburnout symptoms are present, and provide alerts and recommendationsbased on the burnout symptoms.

Referring now to FIG. 2, a block diagram 200 illustrating the systemarchitecture of program for detecting and presenting burnout symptoms inaccordance with one embodiment is depicted. As previously described, theburnout detection and prediction program 108A, 108B (FIG. 1) may detectburnout symptoms by monitoring and receiving burnout indicators.Specifically, the burnout detection and prediction program 108A, 108B(FIG. 1) may monitor and receive burnout indicators by using detectors202 a, 202 b, and 202 c such as laptops/workstations 202 a, mobiledevices/wearable devices 202 b, and external sources 202 c, such asemail servers and cloud repositories. Furthermore, the burnout detectionand prediction program 108A, 108B (FIG. 1) may monitor and receiveburnout indicators, such as user psychological and personality traits,levels of activity, communication tone, work-life balance, and fitnessand sleep activity.

For example, the burnout detection and prediction program 108A, 108B(FIG. 1) may use the detectors 202 a, 202 b, and 202 c to monitor andreceive user psychological and personality traits by using IBM Watson™Personality Insights (IBM Watson and all Watson—based trademarks andlogos are trademarks of International Business Machines Corporationand/or its affiliates) to analyze user speeches and written text incommunications. Furthermore, for example, the burnout detection andprediction program 108A, 108B (FIG. 1) may use the detectors 202 a, 202b, and 202 c to monitor and receive user tones and attitudes associatedwith communications by using IBM Watson™ Tone Analyzer to analyzewritten text, such as emails and documents. Also, for example, theburnout detection and prediction program 108A, 108B (FIG. 1) may use thedetectors 202 a, 202 b, and 202 c to monitor and receive user levels ofactivity by analyzing user email activity and calendar schedule.Additionally, for example, the burnout detection and prediction program108A, 108B (FIG. 1) may use the detectors 202 a, 202 b, and 202 c tomonitor and receive user work-life balance by analyzing user calendarsto determine vacation periods and out-of-office calendar entries. Also,for example, the burnout detection and prediction program 108A, 108B(FIG. 1) may use the detectors 202 a, 202 b, and 202 c, such as wearabledevices, to monitor and receive the user fitness and sleep patterns.Furthermore, the burnout detection and prediction program 108A, 108B(FIG. 1) may include a data store 204 to store the monitored andreceived burnout indicators and the information associated with theburnout indicators.

Thereafter, the burnout detection and prediction program 108A, 108B(FIG. 1) may include predictors 206 a, 206 b, and 206 c, such as serversand cloud servers, that may further include predictor rules to determinewhether burnout symptoms are present based on the monitored and receivedburnout indicators that are stored on the data store 204. For example,for a burnout symptom such as pessimism, the burnout detection andprediction program 108A, 108B (FIG. 1) may include a detector 202 a toanalyze written text for negative sentences and to store the analyzedwritten text on data store 204. Furthermore, the burnout detection andprediction program 108A, 108B (FIG. 1) may include messaging services,such as a publish-subscribe (pub/sub) messaging service 208, to enablethe detectors 202 a, 202 b, and 202 c to communicate with the predictors206 a, 206 b, and 206 c. Therefore, the burnout detection and predictionprogram 108A, 108B (FIG. 1) may use a predictor 206 a that may furtherinclude a predictor rule that detects whether a threshold value of 70%of a user's written text includes negative sentences. As such, based onthe predictor rule associated with the predictor 206 a, the burnoutdetection and prediction program 108A, 108B (FIG. 1) may present analert when it is determined that 70% of the stored written text includesnegative sentences.

Referring now to FIG. 3, an operational flowchart 300 illustrating thesteps carried out by a program for detecting burnout symptoms inaccordance with one embodiment is depicted. At 302, the burnoutdetection and prediction program 108A, 108B (FIG. 1) may receivepredictor rules. Specifically, and as previously described in FIG. 2,the burnout detection and prediction program 108A, 108B (FIG. 1) mayinclude predictors 206 a, 206 b, and 206 c (FIG. 2) that may furtherinclude predictor rules to detect whether burnout symptoms are presentbased on monitored and received burnout indicators that are stored onthe data store 204 (FIG. 2). For example, for a burnout symptom such aslack of sleep, the burnout detection and prediction program 108A, 108B(FIG. 1) may include a predictor 206 b (FIG. 2) that may further includea predictor rule that detects when a user gets less than 42 hours ofsleep per week.

Next, at 304, the burnout detection and prediction program 108A, 108B(FIG. 1) may monitor and receive the burnout indicators. As previouslydescribed in FIG. 2, the burnout detection and prediction program 108A,108B (FIG. 1) may use detectors 202 a, 202 b, and 202 c (FIG. 2), suchas laptops/workstations 202 a (FIG. 2), mobile devices/wearable devices202 b (FIG. 2), and external sources 202 c (FIG. 2), such as emailservers and cloud repositories, to monitor burnout indicators, such asuser psychological and personality traits, levels of activity,communication tone, work-life balance, and fitness and sleep activity.For example, the burnout detection and prediction program 108A, 108B(FIG. 1) may use the detectors 202 a, 202 b, and 202 c (FIG. 2) tomonitor and receive user psychological and personality traits by usingIBM Watson™ Personality Insights to monitor, receive, and analyze userspeeches and written text in communications.

Thereafter, at step 306, the burnout detection and prediction program108A, 108B (FIG. 1) may store the monitored and received burnoutindicators. Specifically, and as previously described in FIG. 2, theburnout detection and prediction program 108A, 108B (FIG. 1) may storethe monitored and received burnout indicators on the data store 204(FIG. 2). For example, the burnout detection and prediction program108A, 108B (FIG. 1) may monitor and receive burnout indicators based onreceived user speeches and written text, received user email activityand calendar entries, and received user fitness and sleep patterns.Next, the burnout detection and prediction program 108A, 108B (FIG. 1)may store the received user speeches and written text, the received useremail activity and calendar entries, and the received user fitness andsleep patterns on the data store 204 (FIG. 2).

Then, at 308, the burnout detection and prediction program 108A, 108B(FIG. 1) may detect burnout events associated with the stored monitoredand received burnout indicators. Specifically, the burnout detection andprediction program 108A, 108B (FIG. 1) may use predictors 206 a, 206 b,and 206 c (FIG. 2) to detect burnout events associated with the storedmonitored and received burnout indicators based on the predictor rules.As previously described at step 302, the burnout detection andprediction program 108A, 108B (FIG. 1) may include predictors 206 a, 206b, and 206 c (FIG. 2) that may further include predictor rules todetermine whether burnout symptoms are present based on the monitoredand received burnout indicators that are stored on the data store 204(FIG. 2). For example, for a burnout symptom such as pessimism, theburnout detection and prediction program 108A, 108B (FIG. 1) may includea detector 202 a (FIG. 2) to monitor and receive written text, and tostore the written text on the data store 204 (FIG. 2). Additionally, theburnout detection and prediction program 108A, 108B (FIG. 1) may includea predictor 206 a (FIG. 2) that may further include a predictor rule todetect when a threshold value of 70% of a user's written text includesnegative sentences. Therefore, based on the predictor rule, the burnoutdetection and prediction program 108A, 108B (FIG. 1) may use thepredictor 206 a (FIG. 2) to detect burnout events such as whether 70% ofthe stored written text includes negative sentences.

Next, at 310, the burnout detection and prediction program 108A, 108B(FIG. 1) may determine burnout symptoms based on the detected burnoutevents. As previously described at step 308, the burnout detection andprediction program 108A, 108B (FIG. 1) may use predictors 206 a, 206 b,and 206 c (FIG. 2) to detect burnout events associated with the storedmonitored and received burnout indicators based on the predictor rules.For example, the burnout detection and prediction program 108A, 108B(FIG. 1) may include a predictor 206 a (FIG. 2) that may include apredictor rule to detect a burnout event such as whether a thresholdvalue of 70% of a user's written text includes negative sentences, andmay include a predictor 206 b (FIG. 2) to detect a burnout event such aswhen a user gets less than 42 hours of sleep per week. As such, based onthe predictors 206 a, 206 b (FIG. 2) and the detected burnout events,the burnout detection and prediction program 108A, 108B (FIG. 1) maydetermine that the burnout symptoms pessimism and lack of sleep may bepresent.

Then, at 312, the burnout detection and prediction program 108A, 108B(FIG. 1) may determine aggregate scores based on the determined burnoutsymptoms. Specifically, the burnout detection and prediction program108A, 108B (FIG. 1) may determine aggregate scores that represent theprobability for burnouts. For example, based on the predictors 206 a,206 b, and 206 c (FIG. 2) and the detected burnout events, the burnoutdetection and prediction program 108A, 108B (FIG. 1) may determineburnout symptoms such as lack of free time, neuroticism, lack of sleep,and aggressive tones of communication may be present. Therefore, theburnout detection and prediction program 108A, 108B (FIG. 1) maydetermine there is a 65% probability for a burnout.

Next, at 314, the burnout detection and prediction program 108A, 108B(FIG. 1) may present alerts and recommendations based on the determinedburnout symptoms and the determined aggregate scores. As previouslydescribed at step 310, the burnout detection and prediction program108A, 108B (FIG. 1) may determine burnout symptoms based on burnoutevents detected by the predictors 206 a, 206 b, and 206 c (FIG. 2).Furthermore, at step 312, the burnout detection and prediction program108A, 108B (FIG. 1) may determine aggregate scores based on thedetermined burnout symptoms. As such, the burnout detection andprediction program 108A, 108B (FIG. 1) may present users with alertsthat may include the determined burnout symptoms and the determinedaggregate scores, as well as recommendations to reduce the burnoutsymptoms. For example, the burnout detection and prediction program108A, 108B (FIG. 1) may recommend not extending work activity beyondcertain hours and getting more sleep. Furthermore, the burnout detectionand prediction program 108A, 108B (FIG. 1) may present the alerts usingmessaging, instant messaging, phone and email communications.

It may be appreciated that FIGS. 2 and 3 provide only illustrations ofone implementation and do not imply any limitations with regard to howdifferent embodiments may be implemented. Many modifications to thedepicted environments may be made based on design and implementationrequirements.

FIG. 4 is a block diagram 400 of internal and external components ofcomputers depicted in FIG. 1 in accordance with an illustrativeembodiment of the present invention. It should be appreciated that FIG.4 provides only an illustration of one implementation and does not implyany limitations with regard to the environments in which differentembodiments may be implemented. Many modifications to the depictedenvironments may be made based on design and implementationrequirements.

Data processing system 800, 900 is representative of any electronicdevice capable of executing machine-readable program instructions. Dataprocessing system 800, 900 may be representative of a smart phone, acomputer system, PDA, or other electronic devices. Examples of computingsystems, environments, and/or configurations that may represented bydata processing system 800, 900 include, but are not limited to,personal computer systems, server computer systems, thin clients, thickclients, hand-held or laptop devices, multiprocessor systems,microprocessor-based systems, network PCs, minicomputer systems, anddistributed cloud computing environments that include any of the abovesystems or devices.

User client computer 102 (FIG. 1), and network server 112 (FIG. 1)include respective sets of internal components 800 a, b and externalcomponents 900 a, b illustrated in FIG. 4. Each of the sets of internalcomponents 800 a, b includes one or more processors 820, one or morecomputer-readable RAMs 822, and one or more computer-readable ROMs 824on one or more buses 826, and one or more operating systems 828 and oneor more computer-readable tangible storage devices 830. The one or moreoperating systems 828, the software program 114 (FIG. 1), the burnoutdetection and prediction program 108A (FIG. 1) in client computer 102(FIG. 1), and the burnout detection and prediction program 108B (FIG. 1)in network server computer 112 (FIG. 1) are stored on one or more of therespective computer-readable tangible storage devices 830 for executionby one or more of the respective processors 820 via one or more of therespective RAMs 822 (which typically include cache memory). In theembodiment illustrated in FIG. 4, each of the computer-readable tangiblestorage devices 830 is a magnetic disk storage device of an internalhard drive. Alternatively, each of the computer-readable tangiblestorage devices 830 is a semiconductor storage device such as ROM 824,EPROM, flash memory or any other computer-readable tangible storagedevice that can store a computer program and digital information.

Each set of internal components 800 a, b, also includes a R/W drive orinterface 832 to read from and write to one or more portablecomputer-readable tangible storage devices 936 such as a CD-ROM, DVD,memory stick, magnetic tape, magnetic disk, optical disk orsemiconductor storage device. A software program, such as a burnoutdetection and prediction program 108A and 108B (FIG. 1), can be storedon one or more of the respective portable computer-readable tangiblestorage devices 936, read via the respective R/W drive or interface 832and loaded into the respective hard drive 830.

Each set of internal components 800 a, b also includes network adaptersor interfaces 836 such as a TCP/IP adapter cards, wireless Wi-Fiinterface cards, or 3G or 4G wireless interface cards or other wired orwireless communication links. The burnout detection and predictionprogram 108A (FIG. 1) and software program 114 (FIG. 1) in clientcomputer 102 (FIG. 1), and the burnout detection and prediction program108B (FIG. 1) in network server 112 (FIG. 1) can be downloaded to clientcomputer 102 (FIG. 1) from an external computer via a network (forexample, the Internet, a local area network or other, wide area network)and respective network adapters or interfaces 836. From the networkadapters or interfaces 836, the burnout detection and prediction program108A (FIG. 1) and software program 114 (FIG. 1) in client computer 102(FIG. 1) and the burnout detection and prediction program 108B (FIG. 1)in network server computer 112 (FIG. 1) are loaded into the respectivehard drive 830. The network may comprise copper wires, optical fibers,wireless transmission, routers, firewalls, switches, gateway computersand/or edge servers.

Each of the sets of external components 900 a, b can include a computerdisplay monitor 920, a keyboard 930, and a computer mouse 934. Externalcomponents 900 a, b can also include touch screens, virtual keyboards,touch pads, pointing devices, and other human interface devices. Each ofthe sets of internal components 800 a, b also includes device drivers840 to interface to computer display monitor 920, keyboard 930, andcomputer mouse 934. The device drivers 840, R/W drive or interface 832,and network adapter or interface 836 comprise hardware and software(stored in storage device 830 and/or ROM 824).

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 5, illustrative cloud computing environment 500 isdepicted. As shown, cloud computing environment 500 comprises one ormore cloud computing nodes 100 with which local computing devices usedby cloud consumers, such as, for example, personal digital assistant(PDA) or cellular telephone 500A, desktop computer 500B, laptop computer500C, and/or automobile computer system 500N may communicate. Nodes 100may communicate with one another. They may be grouped (not shown)physically or virtually, in one or more networks, such as Private,Community, Public, or Hybrid clouds as described hereinabove, or acombination thereof. This allows cloud computing environment 500 tooffer infrastructure, platforms and/or software as services for which acloud consumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 500A-Nshown in FIG. 5 are intended to be illustrative only and that computingnodes 100 and cloud computing environment 500 can communicate with anytype of computerized device over any type of network and/or networkaddressable connection (e.g., using a web browser).

Referring now to FIG. 6, a set of functional abstraction layers 600provided by cloud computing environment 500 (FIG. 5) is shown. It shouldbe understood in advance that the components, layers, and functionsshown in FIG. 6 are intended to be illustrative only and embodiments ofthe invention are not limited thereto. As depicted, the following layersand corresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and burnout detection and prediction 96. Aburnout detection and prediction program 108A, 108B (FIG. 1) may beoffered “as a service in the cloud” (i.e., Software as a Service (SaaS))for applications running on mobile devices 102 (FIG. 1) and may detectburnout symptoms associated with users.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A method for detecting and presenting a pluralityof burnout symptoms associated with at least one user, the methodcomprising: receiving a plurality of predictor rules for monitoring anddetecting a plurality of burnout indicators; monitoring and receivingthe plurality of burnout indicators; storing the monitored and receivedplurality of burnout indicators; detecting a plurality of burnout eventsassociated with the stored monitored and received plurality of burnoutindicators; determining the plurality of burnout symptoms based on thedetected plurality of burnout events; determining at least one aggregatescore based on the determined plurality of burnout symptoms; andpresenting at least one alert and at least one recommendation based onthe determined plurality of burnout symptoms and the determined at leastone aggregate score.
 2. The method of claim 1, wherein the plurality ofburnout indicators are selected from a group comprising at least one ofa plurality of written text, a plurality of emails, a plurality ofcalendar entries, and a plurality of wearable device data.
 3. The methodof claim 1, wherein monitoring and receiving the plurality of burnoutindicators further comprises: using a plurality of detectors selectedfrom a group comprising at least one of at least one workstation, atleast one wearable device, at least one email server, and at least onecloud repository, to monitor and receive the plurality of burnoutindicators.
 4. The method of claim 1, wherein monitoring and receivingthe plurality of burnout indicators further comprises: using IBM Watson™Personality Insights and IBM Watson™ Tone Analyzer to analyze theplurality of burnout indicators.
 5. The method of claim 1, whereindetecting the plurality of burnout events further comprises: using aplurality of predictors to detect the plurality of burnout events basedon the received plurality of predictor rules.
 6. The method of claim 5,wherein detecting a plurality of burnout events further comprises: usinga publish-subscribe messaging service associated with the plurality ofpredictors to detect the plurality of burnout events.
 7. The method ofclaim 1, wherein presenting the at least one alert and the at least onerecommendation further comprises: using at least one of a messagingcommunication, a phone communication, and an email communication topresent the at least one alert and the at least one recommendation.
 8. Acomputer system for detecting and presenting a plurality of burnoutsymptoms associated with at least one user, comprising: one or moreprocessors, one or more computer-readable memories, one or morecomputer-readable tangible storage devices, and program instructionsstored on at least one of the one or more storage devices for executionby at least one of the one or more processors via at least one of theone or more memories, wherein the computer system is capable ofperforming a method comprising: receiving a plurality of predictor rulesfor monitoring and detecting a plurality of burnout indicators;monitoring and receiving the plurality of burnout indicators; storingthe monitored and received plurality of burnout indicators; detecting aplurality of burnout events associated with the stored monitored andreceived plurality of burnout indicators; determining the plurality ofburnout symptoms based on the detected plurality of burnout events;determining at least one aggregate score based on the determinedplurality of burnout symptoms; and presenting at least one alert and atleast one recommendation based on the determined plurality of burnoutsymptoms and the determined at least one aggregate score.
 9. Thecomputer system of claim 8, wherein the plurality of burnout indicatorsare selected from a group comprising at least one of a plurality ofwritten text, a plurality of emails, a plurality of calendar entries,and a plurality of wearable device data.
 10. The computer system ofclaim 8, wherein monitoring and receiving the plurality of burnoutindicators further comprises: using a plurality of detectors selectedfrom a group comprising at least one of at least one workstation, atleast one wearable device, at least one email server, and at least onecloud repository, to monitor and receive the plurality of burnoutindicators.
 11. The computer system of claim 8, wherein monitoring andreceiving the plurality of burnout indicators further comprises: usingIBM Watson™ Personality Insights and IBM Watson™ Tone Analyzer toanalyze the plurality of burnout indicators.
 12. The computer system ofclaim 8, wherein detecting the plurality of burnout events furthercomprises: using a plurality of predictors to detect the plurality ofburnout events based on the received plurality of predictor rules. 13.The computer system of claim 12, wherein detecting a plurality ofburnout events further comprises: using a publish-subscribe messagingservice associated with the plurality of predictors to detect theplurality of burnout events.
 14. The computer system of claim 8, whereinpresenting the at least one alert and the at least one recommendationfurther comprises: using at least one of a messaging communication, aphone communication, and an email communication to present the at leastone alert and the at least one recommendation.
 15. A computer programproduct for detecting and presenting a plurality of burnout symptomsassociated with at least one user, comprising: one or morecomputer-readable storage devices and program instructions stored on atleast one of the one or more tangible storage devices, the programinstructions executable by a processor, the program instructionscomprising: program instructions to receive a plurality of predictorrules for monitoring and detecting a plurality of burnout indicators;program instructions to monitor and receive the plurality of burnoutindicators; program instructions to store the monitored and receivedplurality of burnout indicators; program instructions to detect aplurality of burnout events associated with the stored monitored andreceived plurality of burnout indicators; program instructions todetermine the plurality of burnout symptoms based on the detectedplurality of burnout events; program instructions to determine at leastone aggregate score based on the determined plurality of burnoutsymptoms; and program instructions to present at least one alert and atleast one recommendation based on the determined plurality of burnoutsymptoms and the determined at least one aggregate score.
 16. Thecomputer program product of claim 15, wherein the plurality of burnoutindicators are selected from a group comprising at least one of aplurality of written text, a plurality of emails, a plurality ofcalendar entries, and a plurality of wearable device data.
 17. Thecomputer program product of claim 15, wherein the program instructionsto monitor and receive the plurality of burnout indicators furthercomprises: program instructions to use a plurality of detectors selectedfrom a group comprising at least one of at least one workstation, atleast one wearable device, at least one email server, and at least onecloud repository, to monitor and receive the plurality of burnoutindicators.
 18. The computer program product of claim 15, wherein theprogram instructions to monitor and receive the plurality of burnoutindicators further comprises: program instructions to use IBM Watson™Personality Insights and IBM Watson™ Tone Analyzer to analyze theplurality of burnout indicators.
 19. The computer program product ofclaim 15, wherein the program instructions to detect the plurality ofburnout events further comprises: program instructions to use aplurality of predictors to detect the plurality of burnout events basedon the received plurality of predictor rules.
 20. The computer programproduct of claim 15, wherein program instructions to present the atleast one alert and the at least one recommendation further comprises:program instructions to use at least one of a messaging communication, aphone communication, and an email communication to present the at leastone alert and the at least one recommendation.